博碩士論文 964404002 詳細資訊




以作者查詢圖書館館藏 以作者查詢臺灣博碩士 以作者查詢全國書目 勘誤回報 、線上人數:5 、訪客IP:35.153.135.60
姓名 林品華(Pin-Hua Lin)  查詢紙本館藏   畢業系所 產業經濟研究所
論文名稱 研發資源、學術能量與創新品質
(R&D resources, academic capacity, and innovation quality)
相關論文
★ 期間利差與經濟衰退之預測模型-理性預期假設之驗證★ 台灣、美國總經月數據與台股股價指數之關聯性
★ 台灣資訊電子產業異質性及利潤率之探討★ 中小企業案件逾期放款之預測
★ 台灣半導體產業經營效率分析-三階段資料包絡分析法之應用★ 台灣車輛產業經濟附加價值之研究-兼論影響信通交通器材公司經濟附加價值之因素
★ 外人直接投資與研發活動之關聯性-台灣電子相關產業之實證研究★ 消費性信用貸款授信評量模式之研究
★ 二順位房貸產品風險預警分析★ 新產品商業化流程之個案研究–以美商3M公司為例
★ 高淨值客戶風險屬性與共同基金投資報酬率之實證研究★ 台灣加權指數與指數股票型基金風險值之歷史模擬法分析
★ 國際油價、匯率與利率之動態關聯—VECM與VECM-GARCH之應用★ 主流記憶體之二十年價格模式研究與驗證
★ 以DEA模型分析桃園郵局之營運績效★ 奢侈稅實施對都會地區房價之衝擊反應分析
檔案 [Endnote RIS 格式]    [Bibtex 格式]    [相關文章]   [文章引用]   [完整記錄]   [館藏目錄]   至系統瀏覽論文 (2019-8-1以後開放)
摘要(中) 在國家創新系統中,科學與技術是創新能量的兩個最主要來源,而政府、產業和大學三者間相互連結與協調,更是推動知識的生產、轉化、應用、產業化及升級的主要關鍵。本論文主要從資源投入、學術能量與創新品質之關係出發,以三篇研究來討論創新系統相關文獻上相對較忽略的重點,以豐富對整個連結機制的瞭解。
第一篇研究是探討科學產出與國家生產力的關係。與過去文獻差異處在於,本研究考量相關變數的時間序列特性,以及彼此間存在的時間落後關係,以1982-2007年25國的跨國資料為基礎,更嚴謹地討論學術產出與經濟成長的關係。結果顯示,原始資料皆為非穩態數列,具有高度時間相依,而進一步將國家進行分群討論,發現台灣、韓國、新加坡及日本等亞洲國家,在GDP與論文量兩者呈現互為因果的關係,背後可能隱含產業發展通常領導其科技政策,並且科學技術發展也會提升產業製程及生產力。至於較富有的西方國家,其相對優勢領域集中在距離產業技術應用端較遠之領域,如醫學、生命科學、自然科學等,多數則未呈現出科學產出影響經濟產出的現象。
第二篇研究在探討學術資源投入與學術品質的關係。本研究以學術論文相對影響力(即論文相對被引用率)作為衡量學術品質的指標,有別於過去文獻常用之論文量的量化指標。在模型設計上,考量高等教育研發經費、高等教育研發存量,以及相關落後期變數,並進階考量科學與工程領域及人文社科領域兩者的差異。利用1981-2011年41國的跨國實證結果顯示,無論在科學與工程領域、人文社科領域或全領域,學術研發資源對學術品質有顯著為正的影響,且資源投入具有累積效果,以及時間的遞延效果。亞洲國家與非亞洲國家在學術品質表現上存有差異,符合過去文獻認為不同國家群集間,存在科學結構不一致之現象的論點。
第三篇研究在探討專利品質與國家學術品質之關聯。本研究以平均專利被引用數作為專利品質的衡量變數,從國家學術品質、國家的產學連結、科學與技術連結(科學知識擴散)、技術與技術連結(技術知識擴散)等構面進行討論。利用1995-2012年33國的跨國實證結果顯示,國家學術品質是影響國家專利品質的重要變數,對專利品質有顯著的正面助益。當國家的產學連結、技術連結程度(專利引用程度)越高,也使專利品質愈佳。但在引用非專利文獻相關變數的科學連結部分,實證結果呈現負向,但不顯著的影響,由於引用大量科學文獻的專利,可能包含更複雜與原創性的知識,使得知識應用不易擴散,而領域差異及專利權人屬性等因素可能也會產生引用行為的差異。
摘要(英) In a national innovation system, science and technology are the two most important sources of innovation capacity. The interconnections and coordination between government, industry, and universities are the main factors in promoting the production, transfer, application, industrialization, and improvement of knowledge. This thesis begins with the relationships between resources investment, academic capacity, and innovation quality, conducting three studies to discuss some key points relatively neglected in the literature related to innovation systems, in order to enrich the understanding of the entire linkage mechanism.
The first study explores the relationship between research output and economic productivity. Unlike previous studies, this paper takes into consideration the time-series features of variables and the time-lagged relationships between one another in order to more meticulously discuss the relationship between research output and economic productivity. The empirical results for 25 countries between 1982 and 2007, for Taiwan, Korea, Singapore, Japan, and other Asian countries, when the GDP and the number of paper publications both exhibit mutual causality, indicate that industrial developments are generally leading science and technology policies, and that scientific and technological developments will also enhance industrial processes and productivity. Wealthier Western countries have relative advantages in concentrating on the fields of medicine, life sciences, natural sciences, and other fields far away from industrial applications and the majority of them do not exhibit a situation where research output impacts economic output.
The second study explores the relationship between academic research resources and academic quality. This thesis adopts a paper’s relative citation impact as an indicator of its academic quality, which is unlike the quantitative indicator of the number of paper publications commonly used in previous studies. This paper adopts the cross-country empirical results of ordered probit and panel data models to show that, regardless of whether the field is “science” or “social science” or an entire field, the academic research and development (R&D) resources available have a significantly positive impact on academic quality and that resources investments have cumulative effects and delayed effects. There are differences in the performance of academic quality in Asian and non-Asian countries. These findings are in line with those of past studies, which consider that there are inconsistent scientific structures among the different country groups.
The third study explores the correlation between patent quality and national academic quality. This thesis adopts the average number of forward citations of a patent as a measurable indicator of patent quality and conducts discussions from the perspectives of national academic quality, industry–academia linkages in countries, science and technology linkages (scientific knowledge flows), technology and technology linkages (technological knowledge flows), etc. As indicated by cross-country empirical results for 33 countries from 1995 to 2012, the national academic quality is an important variable impacting national patent quality, having significant positive benefits on patent quality. When the levels of industry–academia linkages and technology linkages (i.e., the number of backward citations) of a country are higher, it will also prompt better patent quality. However, for the science linkage aspect of indicators related to referencing non-patent literature, there are negative albeit insignificant impacts. Since a patent referencing a large number of scientific papers likely contains knowledge with more complexity and originality, it may not be easy to generalize the applications of this knowledge. In addition, factors like differences between fields and the attributes of the patentee may also cause differences in referencing behavior.
關鍵字(中) ★ 研發資源
★ 學術品質
★ 學術相對影響力
★ 專利品質
★ 專利引用
關鍵字(英) ★ R&D resources
★ academic quality
★ relative citation impact
★ patent quality
★ patent citation
論文目次 Chinese Abstract...................i
English Abstract.................iii
Acknowledgements...................v
Table of Content..................vi
List of Figures..................vii
List of Tables...................viii

Chapter 1. Introduction........................................1
1.1 Introduction........................................1
1.2 Research Framework..................................4
Reference...............................................5
Chapter 2. Research Output and Economic Productivity...........9
2.1 Introduction........................................9
2.2 Data & Methodology.................................12
2.3 Empirical Results..................................14
2.4 Conclusion.........................................17
Reference..............................................18
Chapter 3. Academic Research Resources and Academic Quality...28
3.1 Introduction.......................................28
3.2 Related Literature.................................30
3.3 Empirical Model, Methodology and Data..............32
3.4 Empirical Results and Discussion...................37
3.5 Concluding Remarks and Policy Implications.........40
References.............................................42
Chapter 4. Patent Quality and National Academic Quality.......50
4.1 Introduction.......................................50
4.2 Literature Review..................................53
4.3 Empirical Model and Data Processing................56
4.4 Empirical Results..................................61
4.5 Conclusions and Policy Recommendations.............65
Reference..............................................68
Chapter 5. Concluding Remarks.................................78
參考文獻 Acosta, M., & Coronado, D. (2003). Science–technology flows in Spanish regions: an analysis of scientific citations in patents. Research policy, 32(10), 1783-1803.
Acosta, M., Coronado, D., & Martínez, M. Á. (2012). Spatial differences in the quality of university patenting: Do regions matter?. Research Policy, 41(4), 692-703.
Aguillo, I. F., Ortega, J. L., & Fernández, M. (2008). Webometric ranking of world universities: Introduction, methodology, and future developments. Higher education in Europe, 33(2-3), 233-244.
Albarrán, P., Crespo, J. A., Ortuno, I., & Ruiz-Castillo, J. (2010). A comparison of the scientific performance of the US and the European Union at the turn of the 21st century. Scientometrics, 85(1), 329-344.
Albert, M. B., Avery, D., Narin, F., & McAllister, P. (1991). Direct validation of citation counts as indicators of industrially important patents. Research policy, 20(3), 251-259.
Allison, J. R., Lemley, M. A., & Walker, J. H. (2009). Extreme Value or Trolls on Top? The Characteristics of the Most Litigated Patents. University of Pennsylvania Law Review, 158(1).
Allison, J. R., Lemley, M. A., Moore, K. A., & Trunkey, R. D. (2004). Valuable patents. Georgetown Law Journal, 92 (3), 435-479.
Allison, J., Lemley, M., & Walker, J. (2010). Patent quality and settlement among repeat patent litigants. Georgetown Law Journal, 99, 677-712.
Archibugi, D., & Coco, A. (2004). A new indicator of technological capabilities for developed and developing countries (ArCo). World Development, 32, 629-654.
Auranen, O., & Nieminen, M. (2010). University research funding and publication performance—An international comparison. Research Policy, 39(6), 822-834.
Balassa, B. (1965). The liberalization and revealed comparative advantage. The Manchester School, 33, 99-123.
Bloom, N., & Van Reenen, J. (2000). Patents, productivity and market value in a panel of British firms. Institute for Fiscal Studies.
Bornmann, L., & Mutz, R. (2011). Further steps towards an ideal method of measuring citation performance: The avoidance of citation (ratio) averages in field-normalization. Journal of Informetrics, 5(1), 228-230.
Cassiman, B., Veugelers, R., & Zuniga, P. (2008). In search of performance effects of (in) direct industry science links. Industrial and Corporate Change, 17(4), 611-646.
Chan, K. C., Chen, C. R., & Cheng, L. T. (2005). Ranking research productivity in accounting for Asia-Pacific universities. Review of Quantitative Finance and Accounting, 24(1), 47-64.
Chang, Y. C., & Chen, M. H. (2004). Comparing approaches to systems of innovation: the knowledge perspective. Technology in Society, 26(1), 17-37.
Chen, C. P., Hu, J. L., & Yang, C. H. (2013). Produce patents or journal articles? A cross-country comparison of R&D productivity change. Scientometrics, 94(3), 833-849.
Chuang, Y. W., Lee, L. C., Hung, W. C., & Lin, P. H. (2010). Forging into the innovation lead- A comparative analysis of scientific capacity. International Journal of Innovation Management, 14(03), 511-529.
Cincera, M. (1997). Patents, R&D, and technological spillovers at the firm level: some evidence from econometric count models for panel data. Journal of Applied Econometrics, 12(3), 265-280.
Cremers, K. (2004). Determinants of patent litigation in Germany. ZEW-Centre for European Economic Research Discussion Paper, (04-072).
Crespi, G. A., & Geuna, A. (2008). An empirical study of scientific production: A cross country analysis, 1981-2002. Research Policy, 37, 565-579.
Cuneo, P., & Mairesse, J. (1984). Productivity and R&D at the firm level in French manufacturing. In: Griliches, Z. (Ed.). R&D, Patents, and Productivity. University of Chicago Press, Chicago, IL, 375-392.
De Moya-Anegón, F., & Herrero-Solana, V. (1999). Science in America Latina: a comparison of bibliometric and scientific-technical indicators. Scientometrics, 46(2), 299-320.
Desai, M., Fukuda-Parr, S., Johansson, C., & Sagasti, F. (2002). Measuring the technology achievement of nations and the capacity to participate in the network age. Journal of Human Development, 3(1), 95-122.
Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American statistical association, 74(366a), 427-431.
Dickey, D. A., & Fuller, W. A. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica: Journal of the Econometric Society, 1057-1072.
Dill, D.D., & Soo, M. (2005), Academic quality, league tables, and public policy: A cross-national analysis of university ranking systems. Higher Education, 49(4), 495-533.
Docampo, D. (2011). On using the Shanghai ranking to assess the research performance of university systems. Scientometrics, 86(1), 77-92.
Dodgson, M. (2000). Policies for science, technology, and innovation in Asian newly industrializing economies. In: Kim, L. and Nelson, R. (Eds). Technology, Learning, and Innovation: Experiences of Newly Industrializing Economies. New York: Cambridge University Press.
Egghe, L. & Rousseau, R. (1990). Introduction to Informetrics: Quantitative methods in library, documentation and information science. Amsterdam: Elsevier.
Etzkowitz, H. (1998). The norms of entrepreneurial science: cognitive effects of the new university–industry linkages. Research policy, 27(8), 823-833.
Falk, N., & Train, K. (2015). The Relation of Patent Characteristics to Forward Citations. University of California, Berkeley.
Foltz, J., Barham, B., & Kim, K. (2000). Universities and agricultural biotechnology patent production. Agribusiness, 16(1), 82-95.
Freeman, C. (1987). Technology policy and economic performance - Lessons from Japan. London: Pinter Publishers.
Fung, M. K. (2005). Are knowledge spillovers driving the convergence of productivity among firms?. Economica, 72(286), 287-305.
Garfield, E. (1996). The Significant Scientific Literature Appears in a Small Core of Journals. Scientist, 10, 13-15.
Geuna, A., & Martin, B. R. (2003). University research evaluation and funding: An international comparison. Minerva, 41(4), 277-304.
Granger, C. W. (1980). Testing for causality: a personal viewpoint. Journal of Economic Dynamics and control, 2, 329-352.
Granger, C. W., & Newbold, P. (1974). Spurious regressions in econometrics. Journal of econometrics, 2(2), 111-120.
Green, W. (2003). Econometric Analysis(5th ed). Prentice Hall.
Griliches, Z. (1990). Patent statistics as economic indicators: a survey (No. w3301). National Bureau of Economic Research.
Guellec, D., & de la Potterie, B. V. P. (2000). Applications, grants and the value of patent. Economics letters, 69(1), 109-114.
Guellec, D., & Van Pottelsberghe de la Potterie, B. (2002). The value of patents and patenting strategies: countries and technology areas patterns. Economics of Innovation and New Technology, 11(2), 133-148.
Guellec, D., & Van Pottelsberghe de la Potterie, B. (2004). From R&D to productivity growth: Do the institutional settings and the source of funds of R&D matter?. Oxford Bulletin of Economics and Statistics, 66(3), 353-378.
Hall, B. H., & Mairesse, J. (1995). Exploring the relationship between R&D and productivity in French manufacturing firms. Journal of econometrics, 65(1), 263-293.
Hall, B. H., Jaffe, A. B., & Trajtenberg, M. (2001). The NBER patent citation data file: Lessons, insights and methodological tools (No. w8498). National Bureau of Economic Research.
Hall, B. H., Jaffe, A., & Trajtenberg, M. (2005). Market value and patent citations. RAND Journal of economics, 16-38.
Harhoff, D., & Reitzig, M. (2004). Determinants of opposition against EPO patent grants—the case of biotechnology and pharmaceuticals. International journal of industrial organization, 22(4), 443-480.
Harhoff, D., Narin, F., Scherer, F. M., & Vopel, K. (1999). Citation frequency and the value of patented inventions. Review of Economics and statistics, 81(3), 511-515.
Harhoff, D., Scherer, F. M., & Vopel, K. (2003). Citations, family size, opposition and the value of patent rights. Research Policy, 32(8), 1343-1363.
Hausman, J. A., Hall, B. H., & Griliches, Z. (1984). Econometric models for count data with an application to the patents-R&D relationship. Econometrica, 52 (4), 909-38.
Hobday, M. (2000). East versus Southeast Asian innovation systems: Comparing OEM-and TNC-led growth in electronics. Technology, learning, and innovation: Experiences of newly industrializing economies, 129-169.
Hsu, C. H., Luo, Y. L., Lin, P. H., & Chang, S.H. (2012). A National System Shaped by Innovation, Learning and Core Capacities. Taipei: Science & Technology Policy Research and Information Center, National Applied Research Laboratories.
Huang, M. H., & Ho, H. P. (2009). A study of indicators for knowledge originality and knowledge generality in library and information science. Journal of Library and Information Science, 35(2), 14-33.
Hung, W. C., Lee, L. C., & Tsai, M. H. (2009). An international comparison of relative contributions to academic productivity. Scientometrics, 81(3), 703-718.
Hung, W. C., Lee, L. C., & Tsai, M. H. (2009). An international comparison of relative contributions to academic productivity. Scientometrics, 81(3), 703-718.
Ibata-Arens, K. (2008). Comparing national innovation systems in Japan and the United States: Push, pull, drag and jump factors in the development of new technology. Asia Pacific business review, 14(3), 315-338.
index mundi.(2015). World Factbook. Retrieved December 1, 2015, from http://www.indexmundi.com/factbook/
Inglesi-Lotz, R., & Pouris, A. (2013). The influence of scientific research output of academics on economic growth in South Africa: an autoregressive distributed lag (ARDL) application. Scientometrics, 95(1), 129-139.
Inglesi-Lotz, R., Balcilar, M., & Gupta, R. (2014). Time-varying causality between research output and economic growth in US. Scientometrics, 100(1), 203-216.
Jacobs, D., & Ingwersen, P. (2000). A bibliometric study of the publication patterns in the sciences of South African scholars 1981–96. Scientometrics, 47(1), 75-93.
Jaffe, A. B. (1989). Real effects of academic research. American Economic Review, 79(5), 957-970.
Jaffe, A. B., Trajtenberg, M., & Henderson, R. (1993). Geographic localization of knowledge spillovers as evidenced by patent citations. Quarterly journal of Economics, 108(3), 577-598.
Kayal, A. A. (2008). National innovation systems a proposed framework for developing countries. International Journal of Entrepreneurship and Innovation Management, 8(1), 74-86.
Kealey, T. (1996). The Economic Laws of Scientific Research. New York: St. Martin’s Press.
King, D. A. (2004). The scientific impact of nations: What different countries get for their research spending. Nature, 430, 311-316.
Kirman, A., & Dahl, M. (1994). Economic research in Europe. European Economic Review, 38(3), 505-522.
Kivinen, O., Hedman, J., & Kaipainen, P. (2013). Productivity analysis of research in Natural Sciences, Technology and Clinical Medicine: an input–output model applied in comparison of Top 300 ranked universities of 4 North European and 4 East Asian countries. Scientometrics, 94(2), 683-699.
Kocher, M. G., & Sutter, M. (2001). The institutional concentration of authors in top journals of economics during the last two decades. The Economic Journal, 111(472), 405-421.
Kocher, M. G., Luptacik, M., & Sutter, M. (2006). Measuring productivity of research in economics: A cross-country study using DEA. Socio-Economic Planning Sciences, 40(4), 314-332.
Kuhlmann, S., & Arnold, E. (2001). RCN in the Norwegian research and innovation system. Fraunhofer ISI.
Kumar, R. R., Stauvermann, P. J., & Patel, A. (2016). Exploring the link between research and economic growth: an empirical study of China and USA. Quality & Quantity, 50(3), 1073-1091.
Kumari, G. L. (2009). Synthetic organic chemistry research: Analysis by scientometric indicators. Scientometrics, 80(3), 559-570.
Lanjouw, J. O. (1998). Patent protection in the shadow of infringement: Simulation estimations of patent value. The Review of Economic Studies, 65(4), 671-710.
Lanjouw, J. O., & Schankerman, M. (1997). Stylized facts of patent litigation: Value, scope and ownership (No. w6297). National Bureau of Economic Research.
Lanjouw, J. O., & Schankerman, M. (1999). The quality of ideas: measuring innovation with multiple indicators (No. w7345). National Bureau of Economic Research.
Lanjouw, J. O., & Schankerman, M. (2001). Characteristics of patent litigation: a window on competition. RAND journal of economics, 32(1), 129-151.
Lee, L. C., Lin, P. H., Chuang, Y. W., & Lee, Y. Y. (2011). Research output and economic productivity: A Granger causality test. Scientometrics, 89(2), 465-478.
Lee, Y. G., Lee, J. D., Song, Y. I., & Lee, S. J. (2007). An in-depth empirical analysis of patent citation counts using zero-inflated count data model: The case of KIST. Scientometrics, 70(1), 27-39.
Lehvo, A., & Nuutinen, A. (2006). Finnish science in international comparison. A bibliometric analysis. Publications of the Academy of Finland, 15(06).
Lerner, J. (1994). The importance of patent scope: an empirical analysis. The RAND Journal of Economics, 25(2), 319-333.
Lewison, J. G. G. & May, R. M. (1997). Government funding of research and development. Science, 278(5339), 878-880.
Leydesdorff, L., & Etzkowitz, H. (1997). Universities in the global economy: a triple helix of University-Industry-Government Relations. London: Cassell Academic.
Liu, N. C., & Cheng, Y. (2005). The academic ranking of world universities. Higher education in Europe, 30(2), 127-136.
Mansfield, E. (1991). Academic research and industrial innovation. Research policy, 20(1), 1-12.
Mansfield, E. (1995). Academic research underlying industrial innovations: sources, characteristics, and financing. Review of Economics and Statistics, 77(1), 55-65.
Mansfield, E. (1998). Academic research and industrial innovation: An update of empirical findings. Research policy, 26(7), 773-776.
Mansfield, E., & Lee, J. Y. (1996). The modern university: contributor to industrial innovation and recipient of industrial R&D support. Research policy,25(7), 1047-1058.
Marginson, S. (2007). Global university rankings: Implications in general and for Australia. Journal of Higher Education Policy and Management, 29(2), 131-142.
Marsh, I. (1997). Economic governance in industrialising Asia: structure, comparisons and impacts. Australian Graduate School of Management, University of New South Wales.
Mathews, J. A. (1996). High technology industrialisation in East Asia. Journal of Industry studies, 3(2), 1-77.
May, R. M. (1997). The scientific wealth of nations. Science, 275(5301), 793-796.
McMillan, G. S., Narin, F., & Deeds, D. L. (2000). An analysis of the critical role of public science in innovation: the case of biotechnology. Research policy, 29(1), 1-8.
Meyer, M. (2000). Does science push technology? Patents citing scientific literature. Research Policy, 29(3), 409-434.
Meyer, M., Siniläinen, T., & Utecht, J. (2003). Towards hybrid Triple Helix indicators: A study of university-related patents and a survey of academic inventors. Scientometrics, 58(2), 321-350.
Narin, F. (1994). Patent bibliometrics. Scientometrics, 30(1), 147-155.
Narin, F., Hamilton, K. S., & Olivastro, D. (1997). The increasing linkage between US technology and public science. Research policy, 26(3), 317-330.
Narin, F., Rosen, M., & Olivastro, D. (1988). Patent citation analysis: new validation studies and linkage statistics. Science Indicators: their use in science policy and their role in science studies, 14-16.
OECD(1999). Managing national innovation systems. Paris: OECD.
OECD. (2009). The Use and Analysis of Citations in Patents. OECD Patent Statistics Manual, OECD Publishing.
Osuna, C., Cruz-Castro, L., & Sanz-Menéndez, L. (2011). Overturning some assumptions about the effects of evaluation systems on publication performance. Scientometrics, 86(3), 575-592.
Pakes, A., Simpson, M., Judd, K., & Mansfield, E. (1989). Patent renewal data. Brookings papers on economic activity. Microeconomics, 1989, 331-410.
Patsopoulos, N. A., Analatos, A. A., & Ioannidis, J. P. (2005). Relative citation impact of various study designs in the health sciences. Journal of American Medical Association, 293(19), 2362-2366.
Peri, G. (2004). Knowledge flows and productivity. Rivista di Politica Economica, 94(2), 21-59.
Pierce, S. J. (1999). Boundary crossing in research literatures as a means of interdisciplinary information transfer. Journal of the American Society for Information Science, 50(3), 271-279.
Porter, A. L., & Chubin, D. E. (1985). An indicator of cross-disciplinary research. Scientometrics, 8(3-4), 161-176.
Price, D. S. (1978). Toward a model for science indicators. In: Elkana Y., Lederber G J., Merton R. K., Thackray A., & Zuckerman H. (Eds). Toward a Metric of Science: The Advent of Science Indicators. John Wiley & Sons, New York, 69-95.
Rai, L. P., & Lal, K. (2000). Indicators of the information revolution. Technology in Society, 22(2), 221-235.
Roach, M., & Cohen, W. M. (2013). Lens or prism? Patent citations as a measure of knowledge flows from public research. Management Science, 59(2), 504-525.
Sapsalis, E., de la Potterie, B. V. P., & Navon, R. (2006). Academic versus industry patenting: An in-depth analysis of what determines patent value. Research Policy, 35(10), 1631-1645.
Scherer, F. M., & Harhoff, D. (2000). Technology policy for a world of skew-distributed outcomes. Research Policy, 29(4), 559-566.
Squicciarini, M., Dernis, H., & Criscuolo, C. (2013). Measuring patent quality: Indicators of technological and economic value (No. 2013/3). OECD Publishing.
Sterzi, V. (2013). Patent quality and ownership: An analysis of UK faculty patenting. Research Policy, 42(2), 564-576.
Tang, L., & Shapira, P. (2012). Effects of international collaboration and knowledge moderation on China′s nanotechnology research impacts. Journal of Technology Management in China, 7(1), 94-110.
Tantiyaswasdikul, K. (2014). Determinants of Patent Value in US and Japanese University Patents. International Journal of Technical Research and Applications, 2(4), 08-12.
Tijssen, R. J. (2002). Science dependence of technologies: evidence from inventions and their inventors. Research policy, 31(4), 509-526.
Tong, X., & Frame, J. D. (1994). Measuring national technological performance with patent claims data. Research Policy, 23(2), 133-141.
Toynbee, A. J. (1963). Introduction: The Genesis of Civilisations, A Study of History. 12 vols, 1, 3, New York.
Trajtenberg, M. (1990). A penny for your quotes: patent citations and the value of innovations. The Rand Journal of Economics, 21(1), 172-187.
Verbeek, A., Debackere, K., & Luwel, M. (2003). Science cited in patents: A geographic" flow" analysis of bibliographic citation patterns in patents. Scientometrics, 58(2), 241-263.
Vinkler, P. (1992). Research Policy and Publication Productivity. In: Representations of Science and Technology, Proceedings of the International Conference on Science and Technology Indicators, Bielefeld, DSWO Press, Leiden University, 75-91.
Vinkler, P. (2006). Composite scientometric indicators for evaluating publications of research institutes. Scientometrics, 68(3), 629-642.
Vinkler, P. (2008). Correlation between the structure of scientific research, scientific indicators and GDP in EU and non-EU countries. Scientometrics, 74(2), 237-254.
Waltman, L., Calero-Medina,C., Kosten, J., Noyons, E.C.M., Tijssen, R.J.W., van Eck, N.J., van Leeuwen, T.N., van Raan, A.F.J., Visser, M.S., & Wouters, P. (2012). The Leiden Ranking 2011/2012: data collection, indicators, and interpretation. Journal of the American Society for Information Science and Technology, 63(12), 2419-2432
Wang, E. C., & Huang, W. (2007). Relative efficiency of R&D activities: A cross-country study accounting for environmental factors in the DEA approach. Research Policy, 36(2), 260-273.
Watanabe, C., Akaike, S., & Shin, J. H. (2010). Adaptive efficiency of Japan′s national innovation system toward a service oriented economy. Journal of Services Research, 10(1), 7-50.
Weingart, P. (2005). Impact of bibliometrics upon the science system: Inadvertent consequences?. Scientometrics, 62(1), 117-131.
Yoshikane, F. (2013). Multiple regression analysis of a patent’s citation frequency and quantitative characteristics: The case of Japanese patents. Scientometrics, 96(1), 365-379.
指導教授 陳忠榮(Jong-Rong Chen) 審核日期 2016-7-20
推文 facebook   plurk   twitter   funp   google   live   udn   HD   myshare   reddit   netvibes   friend   youpush   delicious   baidu   
網路書籤 Google bookmarks   del.icio.us   hemidemi   myshare   

若有論文相關問題,請聯絡國立中央大學圖書館推廣服務組 TEL:(03)422-7151轉57407,或E-mail聯絡  - 隱私權政策聲明